OpenRaiser/Intern-Atlas huggingface dataset:
Collective Dynamics of Hierarchical Networks
Venue: arXiv cs.CY 2015
Year: 2015

In an increasingly complex, mobile and interconnected world, we face growing threats of disasters, whether by chance or deliberately. Disruption of coordinated response and recovery efforts due to organizational, technical, procedural, random or deliberate attack could result in the risk of massive loss of life. This requires urgent action to explore the development of optimal information-sharing environments for promoting collective disaster response and preparedness using multijurisdictional hierarchical networks. Innovative approaches to information flow modeling and analysis for dealing with challenges of coordinating across multi layered agency structures as well as development of early warnings through social systems using social media analytics may be pivotal to timely responses to dealing with large scale disasters where response strategies need to be viewed as a shared responsibility. How do facilitate the development of collective disaster response in a multijurisdictional setting? How do we develop and test the level and effectiveness of shared multijurisdictional hierarchical networks for improved preparedness and response? What is the role of multi layered training and exercises in building the shared learning space for collective disaster preparedness and response? The aim of this is therefore to determine factors that may be responsible for affecting disaster response.
Energy Management in Storage-Augmented, Grid-Connected Prosumer Buildings and Neighbourhoods Using a Modified Simulated Annealing Optimization
Venue: arXiv cs.AI 2015
Year: 2015

This article introduces a modified simulated annealing optimization approach for automatically determining optimal energy management strategies in grid-connected, storage-augmented, photovoltaics-supplied prosumer buildings and neighbourhoods based on user-specific goals. For evaluating the modified simulated annealing optimizer, a number of test scenarios in the field of energy self-consumption maximization are defined and results are compared to a gradient descent and a total state space search approach. The benchmarking against these two reference methods demonstrates that the modified simulated annealing approach is able to find significantly better solutions than the gradient descent algorithm - being equal or very close to the global optimum - with significantly less computational effort and processing time than the total state space search approach.
The Necessary And Sufficient Condition for Generalized Demixing
Venue: arXiv cs.SY 2015
Year: 2015

Demixing is the problem of identifying multiple structured signals from a superimposed observation. This work analyzes a general framework, based on convex optimization, for solving demixing problems. We present a new solution to determine whether or not a specific convex optimization problem built for generalized demixing is successful. This solution will also bring about the possibility to estimate the probability of success by the approximate kinematic formula.
A Multi-signal Variant for the GPU-based Parallelization of Growing Self-Organizing Networks
Venue: arXiv cs.DC 2015
Year: 2015

Among the many possible approaches for the parallelization of self-organizing networks, and in particular of growing self-organizing networks, perhaps the most common one is producing an optimized, parallel implementation of the standard sequential algorithms reported in the literature. In this paper we explore an alternative approach, based on a new algorithm variant specifically designed to match the features of the large-scale, fine-grained parallelism of GPUs, in which multiple input signals are processed at once. Comparative tests have been performed, using both parallel and sequential implementations of the new algorithm variant, in particular for a growing self-organizing network that reconstructs surfaces from point clouds. The experimental results show that this approach allows harnessing in a more effective way the intrinsic parallelism that the self-organizing networks algorithms seem intuitively to suggest, obtaining better performances even with networks of smaller size.
A Variance Reduced Stochastic Newton Method
Venue: arXiv cs.LG 2015
Year: 2015

Quasi-Newton methods are widely used in practise for convex loss minimization problems. These methods exhibit good empirical performance on a wide variety of tasks and enjoy super-linear convergence to the optimal solution. For large-scale learning problems, stochastic Quasi-Newton methods have been recently proposed. However, these typically only achieve sub-linear convergence rates and have not been shown to consistently perform well in practice since noisy Hessian approximations can exacerbate the effect of high-variance stochastic gradient estimates. In this work we propose Vite, a novel stochastic Quasi-Newton algorithm that uses an existing first-order technique to reduce this variance. Without exploiting the specific form of the approximate Hessian, we show that Vite reaches the optimum at a geometric rate with a constant step-size when dealing with smooth strongly convex functions. Empirically, we demonstrate improvements over existing stochastic Quasi-Newton and variance reduced stochastic gradient methods.
Some Further Evidence about Magnification and Shape in Neural Gas
Venue: arXiv cs.NE 2015
Year: 2015

Neural gas (NG) is a robust vector quantization algorithm with a well-known mathematical model. According to this, the neural gas samples the underlying data distribution following a power law with a magnification exponent that depends on data dimensionality only. The effects of shape in the input data distribution, however, are not entirely covered by the NG model above, due to the technical difficulties involved. The experimental work described here shows that shape is indeed relevant in determining the overall NG behavior; in particular, some experiments reveal richer and complex behaviors induced by shape that cannot be explained by the power law alone. Although a more comprehensive analytical model remains to be defined, the evidence collected in these experiments suggests that the NG algorithm has an interesting potential for detecting complex shapes in noisy datasets.
Towards Efficient Axiom Pinpointing of EL+ Ontologies
Venue: arXiv cs.LO 2015
Year: 2015

The EL family of Description Logics (DLs) has been the subject of interest in recent years. On the one hand, these DLs are tractable, but fairly inexpressive. On the other hand, these DLs can be used for designing different classes of ontologies, most notably ontologies from the medical domain. Unfortunately, building ontologies is error-prone. As a result, inferable subsumption relations among concepts may be unintended. In recent years, the problem of axiom pinpointing has been studied with the purpose of providing minimal sets of axioms that explain unintended subsumption relations. For the concrete case of EL and EL+, the most efficient approaches consist of encoding the problem into propositional logic, specifically as a Horn formula, which is then analyzed with a dedicated algorithm. This paper builds on this earlier work, but exploits the important relationship between minimal axioms sets and minimal unsatisfiable subformulas in the propositional domain. In turn, this relationship allows applying a vast body of recent work in the propositional domain to the concrete case of axiom pinpointing for EL and its variants. From a practical perspective, the algorithms described in this paper are often several orders of magnitude more efficient that the current state of the art in axiom pinpointing for the EL family of DLs.
A Novel Modified Apriori Approach for Web Document Clustering
Venue: arXiv cs.IR 2015
Year: 2015

The traditional apriori algorithm can be used for clustering the web documents based on the association technique of data mining. But this algorithm has several limitations due to repeated database scans and its weak association rule analysis. In modern world of large databases, efficiency of traditional apriori algorithm would reduce manifolds. In this paper, we proposed a new modified apriori approach by cutting down the repeated database scans and improving association analysis of traditional apriori algorithm to cluster the web documents. Further we improve those clusters by applying Fuzzy C-Means (FCM), K-Means and Vector Space Model (VSM) techniques separately. For experimental purpose, we use Classic3 and Classic4 datasets of Cornell University having more than 10,000 documents and run both traditional apriori and our modified apriori approach on it. Experimental results show that our approach outperforms the traditional apriori algorithm in terms of database scan and improvement on association of analysis. We found out that FCM is better than K-Means and VSM in terms of F-measure of clusters of different sizes.
Guided Grammar Convergence
Venue: arXiv cs.SE 2015
Year: 2015

Relating formal grammars is a hard problem that balances between language equivalence (which is known to be undecidable) and grammar identity (which is trivial). In this paper, we investigate several milestones between those two extremes and propose a methodology for inconsistency management in grammar engineering. While conventional grammar convergence is a practical approach relying on human experts to encode differences as transformation steps, guided grammar convergence is a more narrowly applicable technique that infers such transformation steps automatically by normalising the grammars and establishing a structural equivalence relation between them. This allows us to perform a case study with automatically inferring bidirectional transformations between 11 grammars (in a broad sense) of the same artificial functional language: parser specifications with different combinator libraries, definite clause grammars, concrete syntax definitions, algebraic data types, metamodels, XML schemata, object models.
The Complexity-Rate Tradeoff of Centralized Radio Access Networks
Venue: arXiv cs.IT 2015
Year: 2015

In a centralized RAN, the signals from multiple RAPs are processed centrally in a data center. Centralized RAN enables advanced interference coordination strategies while leveraging the elastic provisioning of data processing resources. It is particularly well suited for dense deployments, such as within a large building where the RAPs are connected via fibre and many cells are underutilized. This paper considers the computational requirements of centralized RAN with the goal of illuminating the benefits of pooling computational resources. A new analytical framework is proposed for quantifying the computational load associated with the centralized processing of uplink signals in the presence of block Rayleigh fading, distance-dependent path-loss, and fractional power control. Several new performance metrics are defined, including computational outage probability, outage complexity, computational gain, computational diversity, and the complexity-rate tradeoff. The validity of the analytical framework is confirmed by comparing it numerically with a simulator compliant with the 3GPP LTE standard. Using the developed metrics, it is shown that centralizing the computing resources provides a higher net throughput per computational resource as compared to local processing.
Founding Digital Currency on Imprecise Commodity
Venue: arXiv cs.CY 2015
Year: 2015

Current digital currency schemes provide instantaneous exchange on precise commodity, in which "precise" means a buyer can possibly verify the function of the commodity without error. However, imprecise commodities, e.g. statistical data, with error existing are abundant in digital world. Existing digital currency schemes do not offer a mechanism to help the buyer for payment decision on precision of commodity, which may lead the buyer to a dilemma between having to buy and being unconfident. In this paper, we design a currency schemes IDCS for imprecise digital commodity. IDCS completes a trade in three stages of handshake between a buyer and providers. We present an IDCS prototype implementation that assigns weights on the trustworthy of the providers, and calculates a confidence level for the buyer to decide the quality of a imprecise commodity. In experiment, we characterize the performance of IDCS prototype under varying impact factors.
Falkirk Wheel: Rollback Recovery for Dataflow Systems
Venue: arXiv cs.DC 2015
Year: 2015

We present a new model for rollback recovery in distributed dataflow systems. We explain existing rollback schemes by assigning a logical time to each event such as a message delivery. If some processors fail during an execution, the system rolls back by selecting a set of logical times for each processor. The effect of events at times within the set is retained or restored from saved state, while the effect of other events is undone and re-executed. We show that, by adopting different logical time "domains" at different processors, an application can adopt appropriate checkpointing schemes for different parts of its computation. We illustrate with an example of an application that combines batch processing with low-latency streaming updates. We show rules, and an algorithm, to determine a globally consistent state for rollback in a system that uses multiple logical time domains. We also introduce selective rollback at a processor, which can selectively preserve the effect of events at some logical times and not others, independent of the original order of execution of those events. Selective rollback permits new checkpointing policies that are particularly well suited to iterative streaming algorithms. We report on an implementation of our new framework in the context of the Naiad system.
Interference Prediction in Mobile Ad Hoc Networks with a General Mobility Model
Venue: arXiv cs.IT 2015
Year: 2015

In a mobile ad hoc network (MANET), effective prediction of time-varying interferences can enable adaptive transmission designs and therefore improve the communication performance. This paper investigates interference prediction in MANETs with a finite number of nodes by proposing and using a general-order linear model for node mobility. The proposed mobility model can well approximate node dynamics of practical MANETs. In contrast to previous studies on interference statistics, we are able through this model to give a best estimate of the time-varying interference at any time rather than long-term average effects. Specifically, we propose a compound Gaussian point process functional as a general framework to obtain analytical results on the mean value and moment-generating function of the interference prediction. With a series form of this functional, we give the necessary and sufficient condition for when the prediction is essentially equivalent to that from a Binomial Point Process (BPP) network in the limit as time goes to infinity. These conditions permit one to rigorously determine when the commonly used BPP approximations are valid. Finally, our simulation results corroborate the effectiveness and accuracy of the analytical results on interference prediction and also show the advantages of our method in dealing with complex mobilities.
Encoding Spike Patterns in Multilayer Spiking Neural Networks
Venue: arXiv cs.NE 2015
Year: 2015

Information encoding in the nervous system is supported through the precise spike-timings of neurons; however, an understanding of the underlying processes by which such representations are formed in the first place remains unclear. Here we examine how networks of spiking neurons can learn to encode for input patterns using a fully temporal coding scheme. To this end, we introduce a learning rule for spiking networks containing hidden neurons which optimizes the likelihood of generating desired output spiking patterns. We show the proposed learning rule allows for a large number of accurate input-output spike pattern mappings to be learnt, which outperforms other existing learning rules for spiking neural networks: both in the number of mappings that can be learnt as well as the complexity of spike train encodings that can be utilised. The learning rule is successful even in the presence of input noise, is demonstrated to solve the linearly non-separable XOR computation and generalizes well on an example dataset. We further present a biologically plausible implementation of backpropagated learning in multilayer spiking networks, and discuss the neural mechanisms that might underlie its function. Our approach contributes both to a systematic understanding of how pattern encodings might take place in the nervous system, and a learning rule that displays strong technical capability.
Towards Using Machine Translation Techniques to Induce Multilingual Lexica of Discourse Markers
Venue: arXiv cs.CL 2015
Year: 2015

Discourse markers are universal linguistic events subject to language variation. Although an extensive literature has already reported language specific traits of these events, little has been said on their cross-language behavior and on building an inventory of multilingual lexica of discourse markers. This work describes new methods and approaches for the description, classification, and annotation of discourse markers in the specific domain of the Europarl corpus. The study of discourse markers in the context of translation is crucial due to the idiomatic nature of these structures. Multilingual lexica together with the functional analysis of such structures are useful tools for the hard task of translating discourse markers into possible equivalents from one language to another. Using Daniel Marcu's validated discourse markers for English, extracted from the Brown Corpus, our purpose is to build multilingual lexica of discourse markers for other languages, based on machine translation techniques. The major assumption in this study is that the usage of a discourse marker is independent of the language, i.e., the rhetorical function of a discourse marker in a sentence in one language is equivalent to the rhetorical function of the same discourse marker in another language.
On Convergence and Threshold Properties of Discrete Lotka-Volterra Population Protocols
Venue: arXiv cs.DS 2015
Year: 2015

In this work we focus on a natural class of population protocols whose dynamics are modelled by the discrete version of Lotka-Volterra equations. In such protocols, when an agent $a$ of type (species) $i$ interacts with an agent $b$ of type (species) $j$ with $a$ as the initiator, then $b$'s type becomes $i$ with probability $P\_{ij}$. In such an interaction, we think of $a$ as the predator, $b$ as the prey, and the type of the prey is either converted to that of the predator or stays as is. Such protocols capture the dynamics of some opinion spreading models and generalize the well-known Rock-Paper-Scissors discrete dynamics. We consider the pairwise interactions among agents that are scheduled uniformly at random. We start by considering the convergence time and show that any Lotka-Volterra-type protocol on an $n$-agent population converges to some absorbing state in time polynomial in $n$, w.h.p., when any pair of agents is allowed to interact. By contrast, when the interaction graph is a star, even the Rock-Paper-Scissors protocol requires exponential time to converge. We then study threshold effects exhibited by Lotka-Volterra-type protocols with 3 and more species under interactions between any pair of agents. We start by presenting a simple 4-type protocol in which the probability difference of reaching the two possible absorbing states is strongly amplified by the ratio of the initial populations of the two other types, which are transient, but "control" convergence. We then prove that the Rock-Paper-Scissors protocol reaches each of its three possible absorbing states with almost equal probability, starting from any configuration satisfying some sub-linear lower bound on the initial size of each species. That is, Rock-Paper-Scissors is a realization of a "coin-flip consensus" in a distributed system. Some of our techniques may be of independent value.
Quantitative Approximation of the Probability Distribution of a Markov Process by Formal Abstractions
Venue: arXiv cs.LO 2015
Year: 2015

The goal of this work is to formally abstract a Markov process evolving in discrete time over a general state space as a finite-state Markov chain, with the objective of precisely approximating its state probability distribution in time, which allows for its approximate, faster computation by that of the Markov chain. The approach is based on formal abstractions and employs an arbitrary finite partition of the state space of the Markov process, and the computation of average transition probabilities between partition sets. The abstraction technique is formal, in that it comes with guarantees on the introduced approximation that depend on the diameters of the partitions: as such, they can be tuned at will. Further in the case of Markov processes with unbounded state spaces, a procedure for precisely truncating the state space within a compact set is provided, together with an error bound that depends on the asymptotic properties of the transition kernel of the original process. The overall abstraction algorithm, which practically hinges on piecewise constant approximations of the density functions of the Markov process, is extended to higher-order function approximations: these can lead to improved error bounds and associated lower computational requirements. The approach is practically tested to compute probabilistic invariance of the Markov process under study, and is compared to a known alternative approach from the literature.
A remark on weaken restricted isometry property in compressed sensing
Venue: arXiv cs.IT 2015
Year: 2015

The restricted isometry property (RIP) has become well-known in the compressed sensing community. Recently, a weaken version of RIP was proposed for exact sparse recovery under weak moment assumptions. In this note, we prove that the weaken RIP is also sufficient for \textsl{stable and robust} sparse recovery by linking it with a recently introduced robust width property in compressed sensing. Moreover, we show that it can be widely apply to other compressed sensing instances as well.
Automated Document Indexing via Intelligent Hierarchical Clustering: A Novel Approach
Venue: arXiv cs.IR 2015
Year: 2015

With the rising quantity of textual data available in electronic format, the need to organize it become a highly challenging task. In the present paper, we explore a document organization framework that exploits an intelligent hierarchical clustering algorithm to generate an index over a set of documents. The framework has been designed to be scalable and accurate even with large corpora. The advantage of the proposed algorithm lies in the need for minimal inputs, with much of the hierarchy attributes being decided in an automated manner using statistical methods. The use of topic modeling in a pre-processing stage ensures robustness to a range of variations in the input data. For experimental work 20-Newsgroups dataset has been used. The F- measure of the proposed approach has been compared with the traditional K-Means and K-Medoids clustering algorithms. Test results demonstrate the applicability, efficiency and effectiveness of our proposed approach. After extensive experimentation, we conclude that the framework shows promise for further research and specialized commercial applications.